AI Proof of Concept (PoC)
Test a focused use case before committing to a larger implementation. See how AI performs on your own data, and decide with evidence.
Reduce the risk before you scale the investment.
Will it work on our data?
Demos use clean examples. Your documents and records are messier.
Is it accurate enough?
You need evidence of quality, not a promising impression.
What will it take?
Integration, cost, and effort are hard to estimate without building.
Will people use it?
Adoption depends on how well it fits the real workflow.
What a proof of concept includes.
Success criteria
Agree up front what “good enough” looks like.
Representative data
Test on real examples from your business.
Working prototype
A functional build your team can try, not slides.
Evaluation
Measure quality against the agreed criteria.
User feedback
Put the prototype in front of the people who would use it.
Recommendation
Clear advice to scale, adjust, or stop, with what implementation would involve.
How a proof of concept runs.
Define
Choose the use case, success criteria, and data.
Build
Develop a focused prototype quickly.
Evaluate
Test with real data and users, and measure results.
Decide
Review findings and agree the next step.
Questions about proofs of concept?
How is a PoC different from a pilot?
A proof of concept tests whether the approach works on your data. A pilot puts a production-ready version in front of a wider group of users.
What if the results aren’t good enough?
That’s a valuable outcome. You’ll know why, and whether a different approach, more data, or another use case is the better path.
Can the prototype be reused?
Parts often can. The recommendation explains what would carry forward into implementation.
Do we need a readiness assessment first?
Not if you already have a clear use case and access to the data it needs.